Michael Stumpf

I hold the Chair of Theoretical Systems Biology, and work on a range of topics in theoretical and statistical systems biology, systems medicine, statistical bioinformatics, dynamical systems and networks, and evolutionary theory. My main interests are all related to reverse engineering the structure, dynamics and evolutionary history of biological systems, in particular those processes which enable cells to respond to and interact with their environment. Biological applications include bacterial and fungal host-pathogen interactions, analysis and design of microbial systems, and cellular decision making processes in eukaryotic (including human) stem cell systems.

I originally studied theoretical physics in Tübingen, Göttingen and Oxford. I completed my DPhil in Statistical Physics in Oxford in 1999 and moved straight into Biology. I spent three years as a Wellcome Trust Research Training Fellow in Mathematical Biology in the Department of Zoology in Oxford. In 2002 I took up a Wellcome Trust Career Development Fellowship and moved to the Department of Biology at UCL in London. Since October 2003 I have been based at the Centre for Bioinformatics at Imperial College London.

From October 2004 until December 2007 I was an EMBO Young Investigator. Between March 2006 and February 2009 I was on the BBSRC Engineering and Biological Systems Research Committee. Since October 2008 I have served on the BBSRC Training and Awards Committee. In October 2009 I was awarded a Royal Society Wolfson Research Merit Award.

I am course director of the MSc in Bioinformatics at Imperial College London and am involved with the 3rd year undergraduate option in Systems Biology.

Research Interests

The research in the Theoretical Systems Biology Group uses mathematical, statistical and computational methods to explore functional, evolutionary and statistical problems in systems biology. The methods we use can be applied widely in evolutionary biology, systems biology and bioinformatics, and beyond.

Our research covers several different areas, which include:

Development of inferential procedures for model selection and parameter estimation in complex dynamical systems

Stochastic processes and statistical inference in the evolutionary analysis of biological networks

Sampling properties of random networks

Mathematical and statistical properties of complex stochastic systems and networks

Bayesian analysis of gene regulation

Our work involves extensive computer simulations as well as analytical approaches. We are also interested in analyzing real data and work closely together with both experimental and clinical scientists.